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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Benchmark Analysis

Tremendous progress has been achieved in the development of land models and their inclusion in Earth system models (ESMs). However, we still have very limited knowledge on the performance skills of these land models. This chapter introduces benchmark analysis, which is a procedure to measure performance of models against a set of defined standards. The benchmark analysis includes: (1) defining targeted aspects of model performance to be evaluated; (2) testing model performance in comparison with a set of benchmarks; (3) measuring model performance skill through quantitative metrics; and (4) evaluating model performance and offering suggestions for future model improvement.

Luo, Yiqi↗
DOE OSTI2022

Historical and Future Extreme Event Indices for the Colorado River Basin

Extremes events are occurring more frequently and are projected to increase in the future. This data set includes extreme event indices that were generated for the Colorado River basin using data from the Variable Infiltration Capacity hydrologic model. VIC simulations were run using six CMIP5 earth system models (ESMs), two different time periods, (historical, 1970-1999) and future (2070-2099), and for one Representative Concentration Pathway (RCP 8.5). ESMs available are MPI-ESM-LR, MIROC-ESM, IPSL-CM5A-LR, GFDL-ESM2M, and GFDL-ESM2G. The different indicators include temperature (min/max, freezing days, heating days), precipitation (dry days, max), runoff (min, max), soil moisture (min, max), evapotranspiration (max), snow water equivalent (SWE, max), wind speeds (max), and pressure (min). Detailed information on the indicators, file names, and how they were calculated is included in the README.txt document. Data sets were used to calculate results are in related references Bennett et al. 2020 and Talsma et al. 2021. Details on the VIC model configuration is described in related references Bennett et al. 2018 and Bennett et al. 2017.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Atmosphere-Biosphere Interaction Study (Field Campaign Report)

The objective of this campaign was to collect hyperspectral reflectance measurements and complementary optical and infrared data of land surface (i.e., vegetation and soil) throughout the growing season (i.e., from March to September) at high temporal frequency (e.g., < 30 minutes). To collect the measurements, the EcoSpec system, a tower-based sensor system housing seven optical and infrared sensors that was developed at Argonne National Laboratory, was deployed to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) atmospheric observatory (within the crop, south of the eddy correlation flux measurement system [ECOR] tower). The EcoSpec system consists of a spectroradiometer having 2,151 channels (350–2,500 nm), red-greenblue (RGB) camera, diffuse radiometer, thermal infrared (TIR) sensors, albedometers, Photochemical Reflectance Index (PRI) sensors, Normalized Difference Vegetation Index (NDVI) sensors, and wetness sensor. The system is designed to collect an array of land surface measurements, including hyperspectral reflectance of plants and soils, RGB photos capturing contextual information of land surface, radiant temperature of sky and land surface, incoming and reflected shortwave radiation, and incoming radiation components (direct and diffuse) as frequent as every minute from up to 12 discrete positions around the tower. This would allow us to capture inherent heterogeneity of the terrestrial ecosystem and to obtain representative spectral signatures of the plant and soil surfaces via averaging those measurements. The SGP site provides advanced atmospheric and other physical measurements that are essential for our investigation of ecosystem functions using hyperspectral reflectance measurements of land surfaces. Located in the mid-latitudes, the SGP site exhibits dynamic seasonality and a wide range of meteorological variability and flux properties that make it ideal for studying interactions between near-surface atmosphere and biosphere across a range of environmental conditions. The atmosphere, plants, and soil control terrestrial carbon and water cycles. To make more accurate climate forecasts, researchers need to understand ecosystem dynamics at the biosphere–atmosphere interface. Even though our ability to forecast ecosystem dynamics and climate at both the regional and global scales has advanced significantly, we still do not know how local phenomena affecting water and carbon fluxes—such as diurnal variations—interrelate with large-scale atmospheric/climate phenomena, and vice versa. The lack of a more thorough understanding of ecosystem dynamics and climate interactions is a key knowledge gap for the improvement of earth system models (ESMs).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation and improvement of the parameterization of aerosol hygroscopicity in global climate models using in-situ surface measurements (Final Report)

Aerosols are tiny particles suspended on the atmosphere that can interact with incoming solar radiation and affect the Earth radiative budget. They do so by scattering and absorbing solar radiation, and these properties vary depending on the aerosol size and chemical composition. Moreover, by taking up water from the surrounding air, hygroscopic aerosol particles will grow in size and change their chemical composition, thus modifying their optical properties (scattering and absorption) and their final impact on radiative forcing calculations. An accurate knowledge of aerosol hygroscopicity is crucial for estimating the net radiative impact of aerosols. We took a three-pronged approach to improve our understanding of aerosol hygroscopicity and how it is implemented in Earth system models. In the first part of our project, we developed a benchmark dataset from existing aerosol hygroscopic growth measurements made by tandem nephelometer humidogram systems. We analyzed, using a standardized methodology, observations from 26 in-situ stations around the globe. Measurement data was collected from multiple data providers, reviewed and harmonized to create a consistent dataset of the scattering enhancement factor due to aerosol water uptake. This dataset is archived in several publicly available databases for use by interested researchers. In the second part of the project, we used the benchmark hygroscopicity dataset to perform a global study on aerosol hygroscopicity and aerosol optical properties. Measurements show a global picture of scattering enhancement with larger values for Arctic and marine sites and lower for urban and desert sites. We assessed the RH dependence of aerosol radiative forcing and showed that the overall effect of aerosol hygroscopicity on DARF is an increase in the absolute forcing effect (negative sign) by a factor of up to 4 compared to dry conditions (RH<40%). Finally, we explored using aerosol single scattering albedo (SSA) and scattering Angstrom exponent (SAE) as possible proxies for aerosol hygroscopicity. SSA showed more promise as a surrogate for the scattering enhancement factor than SAE, but neither was ideal. In the third part of the project we evaluated the output of ten Earth system models (ESMs) against the benchmark hygroscopicity dataset. ESMs utilize various schemes for aerosol hygroscopicity which had not been previously tested against observations on a global scale. We found that ESMs currently overestimate scattering enhancement due to hygroscopic growth. Model parameterizations of hygroscopicity and model chemistry are two main factors driving the observed diversity in hygroscopicity simulations among the models. In addition, our study makes several suggestions for modelers, including improving the parameterizations of organic and sea salt aerosol hygroscopicity. Future hygroscopicity model evaluation experiments should include the model data related to particles size which was not available for our study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiscale Land-Atmosphere Interaction in Tropical Ecosystems

A leading source of uncertainty in the magnitude of future climate change arises from feedback between the physical climate system and the carbon cycle. Output from 15 Earth System Models (ESMs) in the Coupled Model Intercomparison Project (CMIP5) shows that when forced by identical emissions, predicted atmospheric CO 2 concentrations in 2100 ranged from 780 ppm to 1120 ppm due to variations in carbon cycle feedback among the models. The resulting difference in radiative forcing of climate is almost 2 W m -2 , comparable to the total uncertainty in the physical climate system for the representative concentration pathway (RCP) 8.5 scenario in 2100. Scaling future carbon sources and sinks to match contemporary atmospheric CO 2 reduced this uncertainty by a factor of five, suggesting that contemporary observations might dramatically constrain future predictions, but their scaling had no mechanistic basis, so it provides little insight on carbon cycle processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling

We posit that AI methods can be leveraged to significantly enhance the predictive skill of forward Earth system modeling (ESM) activities. A hybrid framework incorporating traditional ESM modeling, inference methods, and AI techniques could make better use of both measured and computed information as well as computational resources by targeting inference tasks at program priorities, such as the predictability of precipitation extremes. This runtime pathway to closing the simulation/analysis-data/model improvement loop will streamline the traditional offline pathway to model improvement, which is based on domain science expertise, while suggesting guidance for further observations and measurements.

58 GEOSCIENCES↗
DOE OSTI2021

A new era of observationally-infused E3SM: GANs for unifying imagery archives

This paper presents an idea to develop a “Rosetta Stone” for unifying observations from various satellite or remote sensors into a common format that would vastly advance our ability to exploit existing datasets for improving predictability within Earth System Models (ESMs). While the applications of such a unified archive are broad, we believe it will be a critical step toward ushering in a new generation of ESMs that are richly informed, guided by, and validated by extensive observational data. With the vast quantity of both remotely-sensed and in-situ data streams available and coming online, new approaches are needed that can harmonize and thus fully exploit these expensive datasets. While we present the broader idea, we point to examples of applications that impact the water cycle and its representation in ESMs.

58 GEOSCIENCES↗
DOE OSTI2021

Characterization of Extreme Hydroclimate Events in Earth System Models using ML/AI

Focal Area(s): (1) We put forward the concepts of data assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization and unsupervised learning applied to downscale information within Earth System Models (ESMs). (2) We discuss predictive modeling through the use of AI techniques and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection) to improve the characterization of extreme hydroclimate events in ESMs. The AI-based models will run five-six order of magnitude times faster, yet will provide similar accuracy, allowing us to provide range bounds on uncertainty faster and thus enabling faster extreme event identification. (3) Further, we consider the insight gleaned from complex data (observed/simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI to improve the characterization of extreme hydroclimate events in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

End-to-End Differentiable Modeling and Management of the Environment

Focal Area: (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization. We emphasize the importance of leveraging optimization techniques from AI/machine learning (ML) to solve challenging problems in Earth system modeling. Science Challenge: Automatic differentiation has had a transformative effect on ML by allowing the calculation of gradients of arbitrary functions in an incredibly large class of models. We can potentially realize similar improvements in parameter estimation and control for Earth system models (ESMs) by reimplementing them in computational frameworks from ML. Practitioners working with large (>10 7 parameters) models in ML and AI can obtain good predictive performance in a range of spatiotemporal tasks by making use of optimization via stochastic gradient descent and incorporating prior knowledge at multiple levels. We propose writing ESMs in open-source computational frameworks such as Torch, Tensorflow, and JAX to greatly expand the scope of environmental forecasting and management challenges, which can be addressed by leveraging automatic differentiation and gradient descent-like algorithms. We do not call for a wholesale replacement of physical models with data-driven surrogates, but rather advocate for interleaving physical and empirical equations in a manner that is most faithful to the extent of our scientific knowledge and observational data. Central to this topic is the merging of differentiable physical simulations with differentiable optimization layers, which are now both beginning to come to the forefront.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geophysical Retrievals in an Artificial Intelligence (AI) Framework for Illuminating Processes Controlling Water Cycle

Focal Area(s): This white paper responds to Focal Area #3: Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: This white paper addresses the water-cycle and data-model integration grand challenge. It leverages data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, and Next-Generation Ecosystem Experiment (NGEE), and Science Focus Area (SFA). The white paper focuses on controlling cloud, precipitation, and radiative properties as observed and simulated by the Earth System Models (ESM). The described framework can be readily applied to any other ensemble of instruments, including satellites and other ground-based networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Learned implicit representations of aerosol chemistry and physics for enhancing the predictability of water cycle extreme events

Focal Area(s): Focal area 2: Predictive modeling through the use of AI-derived model components; and Focal area 3: physics-guided AI. Science Challenge: Comprehensive models of many geophysical processes require a large number of state variables, disqualifying them for inclusion in Earth System Models (ESMs) for the foreseeable future. This white paper proposes the paradigm shift from the use of state variables representing explicit physical quantities to the use of machine-learning to create and integrate a compact, implicit representation of a physical system with a smaller number of state variables. As an initial application, we propose to create parsimonious machine-learned surrogate models of gas- and aerosol-phase chemistry and physics with the purpose of improving the accuracy of the representation of cloud formation and cloud-aerosol interaction in the E3SM model. This type of improved representation of cloud microphysics is critical for enhancing the predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Predictability and feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model

This white paper responds to Focal Area 2. We seek to build predictive models of leaf and surface conductance of water by implementing deep learning (DL) data assimilation techniques. These new models would then be implemented in existing Land Surface Models (LSMs) and Earth System models (ESMs), generating novel water cycle feedbacks. In doing so, we would improve predictability of expected changes in land precipitation, soil moisture, and vegetation dynamics in the long-term, and the role of land cover on the impacts and feedbacks of extreme weather events in the short-term

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using AI to build a hydrobiogeochemical soil model

Soil water content is a function of inputs from precipitation and outputs via evaporation, transpiration, lateral flow, and vertical percolation, and is sensitive to biogeochemical processes. As such, soils serve as an ideal integrator of atmospheric, hydrological, and biogeochemical processes affecting the water cycle. In addition, soil water retention capacity, infiltration rates, and hydraulic conductivity can buffer or exacerbate the effects of extreme precipitation events (e.g., flooding, runoff, subsurface transport, erosion, greenhouse gas emissions) and mitigate the impact of droughts and heat waves on land systems (e.g., fire, crop failure). However, integrating water cycle measurements spanning different land atmosphere compartments across scales is a fundamental barrier for numerical model predictability. A significant challenge is that each domain (soil, hydrology, biology, and atmosphere) typically collects different sets of data at different temporal and spatial frequencies/scales, and even different dimensionalities (2D vs 3D). To implement soil as an integrator of the water cycle in land models, we suggest that novel machine learning (ML) tools can be developed to effectively simulate complex landscapes across various domains and scales, extended to regions with sparse or no data. The ultimate goals are to improve predictive understanding of land-atmosphere interactions and to extend the predictability of current Earth System Models (ESMs) through better integration of hydrological and biogeochemical data. We envision a framework in which: (1) ML-aided data reconstructions enable the merger of data sources into a unified geospatial product; (2) automated detection techniques are used to improve the knowledge of complex soil processes and interactions; and (3) this knowledge is leveraged and incorporated into models through AI-based emulators to distinctly connect the land and atmospheric compartments of the water cycle in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes

The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface Atmosphere Integrated Field Laboratory (SAIL) Science Plan

Mountains are the natural water towers of the world, effectively turning water vapor into readily available fresh water through precipitation, snowpack, and runoff. Unfortunately, Earth system models (ESMs) have persistently been unable to predict the timing and availability of water resources from mountains because the source(s) of model error are difficult to isolate in complex terrain with limited atmospheric or land-surface observations. Further complications arise from the gross scale mismatch between ESM grid box sizes and the relevant scales of mountainous hydrological processes. The mountain hydrometeorology community has repeatedly called for integrated atmospheric and land observations of water and energy budgets in complex terrain that span these scales to establish benchmarks against which scale-dependent models can be further developed.

54 ENVIRONMENTAL SCIENCES↗